With the development of the Internet and the economy globalization, e-commerce has become a main marketing method. Various e-commerce websites on the Internet conduct business through either a vertical sale model or a comprehensive sale model. No matter what type the e-commerce website is, a detailed and fixed categorization system is needed to manage a large quantity of products published at the websites.
When a seller publishes a product, the seller needs to categorize the product into a specific category in the categorization system. Large e-commerce websites, especially e-commerce websites of a comprehensive sale type contain a large number of and different types of products and thus their categorization systems are large. It is not easy for a user to choose a category corresponding to the product from a large number of categories.
The conventional e-commerce websites use hierarchical categorizations that require the seller to select a most relevant category one level after another. With respect to a large e-commerce website with a large categorization system, it is not easy for the seller to choose the category by himself/herself according to levels of categories. In addition, if the seller selects an irrelevant category, it is not beneficial for the e-commerce website to manage products by categories and it affects buyers' experiences of searching products and the buyer's opportunity to present the product.
Some e-commerce websites recommend categories based on input keywords and their relevancy to category names. Such a method for recommending a category based on text relevancies of the input keywords reduces the difficulty of the seller to find categories to some extent. However, if the inquiry word input by the seller does not textually match a category name of a most relevant category, it will find no category or a wrong category and it will be difficult for the e-commerce website to classify products. In addition, under such a method for recommending a category, the e-commerce website administrator sets keywords relevant to the categories based on his/her own preset configuration rules. Such configuration rules, however, cannot reflect historical click information of the buyers to each category. Thus, the recommended category to the seller may not be the most interesting category to the buyer. If the seller publishes the product information based on such inaccurately recommended category, a number of times that the buyers click the published product information is low. That is, a return rate of the published product information is low.
The conventional techniques cause a low return rate of the published product information due to the inaccurate recommended category by the website to the seller when the seller publishes the product information.